Distributed full-duplex non-orthogonal multiple access Internet of Vehicles wireless energy-carrying transmission method

Abstract

The invention discloses a distributed full-duplex non-orthogonal multiple access Internet of Vehicles wireless energy-carrying transmission method, which is based on a distributed full-duplex non-orthogonal multiple access Internet of Vehicles system, aims at minimizing distribution power, and considers power distribution constraint, signal-to-noise ratio constraint, energy collection constraint and power distribution factor constraint. Parameters such as a beam forming matrix, a power distribution factor, a power distribution coefficient, an uplink power distribution coefficient, a self-interference coefficient and energy conversion efficiency are subjected to joint optimization, and a zero-forcing beam forming and dirty paper coding method is utilized to eliminate a multi-user interference item, so that an original optimization problem can be further simplified; and respectively obtaining an optimal solution and a suboptimal solution of the optimization problem by using an interior point method and an analytical method. According to the energy-carrying transmission method, information transmission can be directly completed through V2V and V2X transmission links, users can directly share information with each other, the delay performance is further enhanced, the system spectrum efficiency performance is improved, and different QoS requirements of multiple users are met at the same time.

Description

Technical Field

[0001] The present invention belongs to the technical field of wireless communications, and in particular relates to a distributed full-duplex non-orthogonal multiple access vehicle networking system wireless energy transmission method. Background Art

[0002] With the rapid development trend of autonomous vehicles and 5G, Cellular Vehicle-to-Everything (C-V2X) has been introduced. This communication mode uses cellular networks to achieve seamless and instant communication between vehicles, pedestrians, infrastructure, and everything in between. C-V2X has the advantages of improved communication performance and seamless integration with cellular networks over DSRC.

[0003] In C-V2X systems, cellular networks play a leading role. That is, cellular networks are the backbone for seamless communication and connectivity between vehicles, infrastructure, pedestrians, and other elements of the transportation ecosystem. Cellular technology, especially 4G LTE and the emerging 5G standard, provides a powerful high-performance platform for C-V2X applications, providing V2V, vehicle-to-network (V2N), vehicle-to-infrastructure (V2I), and vehicle-to-pedestrian (V2P) communications. In this case, information transmission is done with the help of cellular. With the so-called large-scale and even rapid growth of 5G connected devices, this architecture is facing increasing challenges. This is mainly attributed to the rapidly growing cellular network load and the strict latency requirements in C-V2X. How to reduce cellular load and reduce communication latency has become a key issue in the deployment of C-V2X.

[0004] On the other hand, SWIPT has been widely studied in energy-constrained wireless networks. SWIPT holds great promise for a variety of applications, ranging from wireless communication systems to the Internet of Things (IoT). In C-V2X systems, the limited energy reserves of vehicles and other mobile devices have always been a challenge for technological innovation and engineering design. In this regard, SWIPT can be an effective solution that helps overcome energy limitations and improve the sustainability and efficiency of equipment. For autonomous vehicles, SWIPT provides a more flexible charging and communication solution, enhancing their autonomy. By utilizing the energy from existing communication signals in the environment, SWIPT also provides a more environmentally friendly method of energy harvesting, which makes it an ideal solution for C-V2X applications.

[0005] There are several significant deficiencies and defects in the current research on SWIPT in C-V2X systems. Many studies are still limited to centralized architectures, which will bring excessive cellular load and higher latency performance when the number of devices increases, making the system's performance in high-concurrency scenarios unsatisfactory. In addition, although existing studies have explored the application of multiple-input multiple-output (MIMO) systems in signal decoding and energy harvesting, related experiments are often conducted in centralized base station settings and fail to effectively consider the dynamic interactions between distributed devices. When it comes to the combination of full-duplex (FD) and non-orthogonal multiple access (NOMA), existing research is also relatively lacking, especially when these technologies are applied to distributed C-V2X systems. Although FD technology can reduce latency by sending and receiving data simultaneously, it is difficult to fully utilize its advantages without in-depth exploration of the power allocation mechanism. In addition, NOMA technology can improve spectral efficiency and energy efficiency, but the content in power allocation is still relatively limited, especially in real-time application scenarios, it is not clear how to effectively allocate resources to meet the needs of different users. Existing research lacks understanding of the complexity of power allocation in SWIPT mode. Although some studies have attempted to solve the power allocation problem, there is a general lack of effective strategies to balance energy harvesting and signal decoding, resulting in the overall system performance not being optimal. Therefore, there is an urgent need for more in-depth research in distributed C-V2X systems to explore effective power allocation strategies in SWIPT scenarios, optimize latency performance and reduce network load, thereby improving the reliability and efficiency of the overall system. Summary of the invention

[0006] In view of this, in order to solve the shortcomings of the above-mentioned prior art, the purpose of the present invention is to provide a distributed full-duplex non-orthogonal multiple access vehicle network (FD-NOMA-V2X) wireless energy transmission method, to solve the optimal solution and suboptimal solution of power allocation, signal-to-noise ratio, energy collection and power allocation factor threshold parameters that minimize the allocated power. This power allocation strategy can optimize delay performance and reduce network load, effectively improving the reliability and efficiency of the overall system.

[0007] To achieve the above purpose, the technical solution adopted by the present invention is: A distributed full-duplex non-orthogonal multiple access vehicle networking wireless energy transmission method, the method is based on a distributed full-duplex non-orthogonal multiple access vehicle networking system, the system includes K+M vehicle networking devices, each vehicle networking device has a cache, and information transmission can be directly completed through vehicle-to-vehicle (V2V) and / or V2X transmission links; the method includes the following steps: Step 1: Consider the process from the mth device to the kth device as a downlink, and the process from the kth device to the mth device as a downlink, and determine the uplink information and the SINR of the receiver; wherein m=1...M, k=1...K; Step 2: With the goal of minimizing the allocated power, establish optimization models for power allocation, signal-to-noise ratio, energy collection, and power allocation factors, and determine the optimization problem; Step 3: Zero-forcing beamforming and dirty paper coding (ZFBF-DPC) is used to eliminate multi-user interference and further simplify the original optimization problem; Step 4: Find the optimal solution and suboptimal solution of the optimization problem, including the following contents: A. Use semidefinite relaxation technology to transform the optimization problem into a convex problem and then solve it by the interior point method. The optimal solution of the power allocation coefficient and the beamforming vector is obtained by the interior point method. B. Assuming that the power allocation coefficient satisfies the equality constraint, the problem is simplified to solving a quadratic equation, and the suboptimal solution of the power allocation coefficient and the beamforming vector is obtained according to the analytical method.

[0008] Furthermore, in step 1, the received signal power is divided into a signal decoding part SD and an energy acquisition part EH. Given γ k (0<γ k <1) as the SD power part, the remaining 1-γ k Part will be used as the EH power part, and the received signal is: in, are the channel matrix, the corresponding beamforming matrix, the downlink transmit signal and the normalized downlink channel noise, respectively. ζ k represents the allocated power coefficient of user k.

[0009] Furthermore, with the goal of minimizing the allocated power, considering the power allocation constraint, signal-to-noise ratio constraint, energy collection constraint and power allocation factor constraint, the optimization model in step 2 is constructed as follows: subject to: Among them, C1 represents the power allocation threshold, C2 represents the signal-to-noise ratio threshold, C3 represents the energy collection threshold, and C4 represents the value range constraint of the power allocation coefficient; G is defined as: in, is the uplink transmission power allocated to user k, η kis the self-interference coefficient of user k; is the total power coefficient value of all users, ζ i represents the allocation power coefficient of user i, P k The power obtained from the transmitter for each user k.

[0010] Furthermore, ZFBF-DPC can eliminate multi-user interference when the downlink channel state information (CSI) is fully known to the transmitter, that is, Rewrite problem P1 as follows: subject to:

[0011] Furthermore, by defining Convert the non-convex problem in P2 into a convex problem, and then use the interior point method to obtain the optimal solution exist Under the condition of Perform eigenvalue decomposition (EVD) to find the optimal beamforming vector

[0012] Furthermore, assuming that w k and γ k The suboptimal value of will lead to the equality of the first two constraints in problem P2, and a suboptimal solution is proposed by analytical method.

[0013] The beneficial effects of the present invention are: The transmission method of the present invention studies the efficient SWIPT transmission based on the decentralized full-duplex non-orthogonal multiple access V2X (FD-NOMA-V2X) system model. All participating devices can cache and share content through direct links for subsequent users to use, even without the help of the cellular network, which can reduce the load and delay of the cellular network; the FD mode can send and receive signals at the same time, further improving the efficiency and throughput of the C-V2X system, and solving the challenging problems of cellular network load, system delay and battery capacity limitation of V2X equipment in the prior art; The present invention considers a decentralized FD-NOMA-V2X system model, which reduces the load of the cellular network by utilizing direct link communication between devices; in addition, NOMA can further improve the spectrum efficiency (SE) performance of the system, and the FD mode can more accurately describe the bidirectional transmission between devices compared with the half-duplex (HD) mode; The efficient SWIPT transmission is transformed into an optimization problem. Its optimal solution is firstly given by the interior point method, and then a suboptimal solution is given by the analytical method. The solution has lower computational complexity and is more feasible in practical applications. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0015] Figure 1 is a system model of the method of the present invention; Figure 2 is a flow chart of the method of the present invention; Figure 3 The β pair of the method of the present invention for solving problem P2 The change curve. DETAILED DESCRIPTION

[0016] The following specific examples are given to further clearly, completely and in detail illustrate the technical solution of the present invention. This example is the best example based on the technical solution of the present invention, but the protection scope of the present invention is not limited to the following examples.

[0017] Example 1 A distributed full-duplex non-orthogonal multiple access vehicle network wireless energy transmission method, the method is based on a distributed full-duplex non-orthogonal multiple access vehicle network system, the system includes K vehicle network devices and M vehicle network devices, with the help of cache content in the device cache, information transmission can be directly completed through the transmission link of the vehicle network; each vehicle network device has a cache and works in full-duplex mode, the whole system adopts non-orthogonal multiple access, M vehicle network devices transmit signals to K vehicle network devices as a downlink, K vehicle network devices transmit signals to M vehicle network devices as an uplink, and each vehicle network device uses a power divider to divide the signal power received by each into two parts: signal decoding and energy acquisition.

[0018] With the help of cached contents in the device cache, information transmission can be directly completed through direct vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2X) transmission links. With the help of cached contents in the device cache, information transmission can be directly completed through direct vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2X) transmission links. In this case, the cellular network is not required, so the load on the cellular network is reduced. The NOMA encoding / decoding scheme can further improve the system spectral efficiency (SE) performance while meeting the different quality of service (QoS) requirements of multiple users. In addition, using full-duplex (FD) mode, users in the system can directly share information with each other to improve the driving experience and alert potential emergency events. Due to the shorter transmission distance compared to cellular-based architectures, this system model can also further enhance latency performance;

[0019] The method comprises the following steps: Step 1: Consider the process from the mth device to the kth device as a downlink, and the process from the kth device to the mth device as a downlink, and determine the uplink information and the SINR of the receiver; wherein m=1...M, k=1...K; The received signal power is divided into SD part and EH part by the equipped power divider device; given γ k (0<γ k <1) as the SD power part, the remaining 1-γ k Part will be used as the EH power part, and the received signal is: in, are the channel matrix, the corresponding beamforming matrix, the downlink transmit signal and the normalized downlink channel noise, respectively. ζ k represents the allocation power coefficient of user k; On the contrary, the uplink information sent by user k to the opposite M users through the FD mode is: in, is the uplink information transmitted. Similarly, in FD mode, we will are the allocated power for uplink transmission from user k, the uplink channel, the corresponding beamforming matrix, and the uplink channel noise, respectively; Without loss of generality, we assume that the channel from the source to the destination follows block fading. Therefore, the SINR of receiver k can be expressed as: in, is the uplink transmission power allocated to user k, η k is the self-interference coefficient of user k; G is defined as: In FD mode, i represents the allocated power coefficient of user i. In addition, is the total power coefficient value of all users. Conversely, the power obtained by each user k from the transmitter can be expressed as: It is worth noting that the first part of G is the correlated interference from the co-channel NOMA users, the second part is the channel noise (i.e., the normalized value), and the third part is the interference from the adjacent channels.

[0020] Step 2: With the goal of minimizing the allocated power, establish optimization models for power allocation, signal-to-noise ratio, energy collection, and power allocation factors, and determine the optimization problem; By w k ,γ k The joint design is used to minimize the allocated power, and the optimization model in step 2 is constructed as: subject to: Among them, C1 represents the power allocation threshold, C2 represents the signal-to-noise ratio threshold, C3 represents the energy collection threshold, and C4 represents the value range constraint of the power allocation coefficient.

[0021] Step 3: Use zero-forced beamforming and dirty paper coding to eliminate multi-user interference and further simplify the original optimization problem; When the downlink channel state information (CSI) is fully known to the transmitter, ZFBF-DPC can eliminate multi-user interference, that is, Problem P1 can be rewritten as: subject to: Among them, μ k represents the energy conversion efficiency of device k.

[0022] Step 4, solving the optimal solution and suboptimal solution of the optimization problem; Step A: using semidefinite relaxation technology to transform the optimization problem into a convex problem and then solving it by interior point method, and obtaining the optimal solution of power allocation coefficient and beamforming vector by interior point method; If and only if When , a feasible solution to problem P2 exists; definition Convert the non-convex problem in P2 into a convex problem: subject to: Z k ≥0 Problem P3 is a convex problem because it has a convex objective and convex constraints. Then, the optimal solution to problem P3 is The optimal solution to problem P3 can be obtained by using the semidefinite relaxation (SDR) technique based on the CVX solver. is also the optimal solution to problem P2. Otherwise, if we have In fact, the rank constraint is always true. Therefore, we can solve problem P3 to obtain The following steps are involved: a1. Initialization; a2. If Not feasible, exit; a3. If Using CVX to solve this problem, we get a4. Under the conditions of Perform eigenvalue decomposition to solve for the optimal beamforming vector a5. Obtain the optimal solution exist Under the condition of Perform eigenvalue decomposition (EVD) to find the optimal beamforming vector

[0023] Furthermore, we first need to check the SINR target If we have Then, when solving problem P3, we can use the CVX tool with the interior point method to obtain optimal solutions, which are also optimal solutions to problem P2. Otherwise, there is no feasible solution. As we have noticed, the computational complexity of the interior point method is where n is the number of inequalities in the problem and ε is the required accuracy of the solution. In addition, it is well known that for a given a×a matrix, the computational complexity of EVD is Then the computational complexity of solving the P2 problem will be

[0024] Step B: Assuming that the power allocation coefficient satisfies the equality constraint, the problem is simplified to solving a quadratic equation, and a suboptimal solution of the power allocation coefficient and the beamforming vector is obtained according to an analytical method, including the following steps: Assuming that the power allocation coefficient satisfies the equality constraint, let w k and γ k The suboptimal value of will lead to the equality of the first two constraints in problem P2, and problem P2 is transformed into: subject to: Since the given constraints are equality, and only w k ,γ k is an unknown parameter, so we can solve this problem directly. The suboptimal solution to problem P2, and the solution to problem P4 is: Solving problem P4 according to the analytical method includes the following sub-steps: b1. Initialization; b2. Let H\k=[h1,...,h1,h k+1 ,...,h K ], b3. If Not feasible, exit; b4. If make b5. Use Calculate the suboptimal solution separately

[0025] Similar to the solution of problem P3, the SINR constraint should also be verified in advance. The computational complexity of solving the quadratic polynomial is The computational complexity of EVD discussed previously is Then the computational complexity of solving the P4 problem will be

[0026] The numerical simulation results are as follows Figure 3 show: 1) As the EH threshold increases, the power splitting coefficient value of the interior point method coincides with the power splitting coefficient value of the analytical method at a certain point; this is the point where the suboptimal solution of the analytical method is equal to the optimal solution; 2) The time consumed using the interior point method is about 1000 times higher than that of the analytical method; this demonstrates the effectiveness of our proposed method in reducing processing time and complexity, making it a feasible solution in the real world.

[0027] The above shows and describes the main features, basic principles and advantages of the present invention. It should be understood by those skilled in the art that the present invention is not limited by the above embodiments. The above embodiments and descriptions are only for explaining the principles of the present invention. Without departing from the spirit and scope of the present invention, the present invention may have various changes and improvements according to actual conditions, and these changes and improvements fall within the scope of the present invention to be protected. The scope of protection of the present invention is defined by the attached claims and their equivalents.

Claims

1. A distributed full-duplex non-orthogonal multiple access vehicle networking wireless energy transmission method, the method is based on a distributed full-duplex non-orthogonal multiple access vehicle networking system, the system includes K+M vehicle networking devices, each vehicle networking device has a cache; characterized in that: The method comprises the following steps: Step 1: Consider the process from the mth device to the kth device as a downlink, and the process from the kth device to the mth device as a downlink, and determine the uplink information and the SINR of the receiver; wherein m=1...M, k=1...K; Step 2: With the goal of minimizing the allocated power, establish optimization models for power allocation, signal-to-noise ratio, energy collection, and power allocation factors, and determine the optimization problem; Step 3: Use zero-forcing beamforming and dirty paper coding to eliminate multi-user interference and further simplify the original optimization problem; Step 4: Find the optimal solution and suboptimal solution of the optimization problem, including the following contents: A. Use semidefinite relaxation technology to transform the optimization problem into a convex problem and then solve it by the interior point method. The optimal solution of the power allocation coefficient and the beamforming vector is obtained by the interior point method. B. Assuming that the power allocation coefficient satisfies the equality constraint, the problem is simplified to solving a quadratic equation, and the suboptimal solution of the power allocation coefficient and the beamforming vector is obtained according to the analytical method.

2. The distributed full-duplex non-orthogonal multiple access vehicle network wireless energy transmission method according to claim 1, characterized in that: In step 1, the received signal power is divided into a signal decoding part and an energy acquisition part, and γ is defined k (0<γ k <1) as the SD power part, the remaining 1-γ k Part will be used as the EH power part, and the received signal is: in, are the channel matrix, the corresponding beamforming matrix, the downlink transmit signal and the normalized downlink channel noise, respectively. ζ k represents the allocated power coefficient of user k; it is assumed that the channel from the source to the destination follows block fading; The SINR of receiver k can be expressed as: in, is the uplink transmission power allocated to user k, η k is the self-interference coefficient of user k; G is defined as: In FD mode, i represents the allocated power coefficient of user i; the first part of G is the correlated interference from the same channel NOMA user, the second part is the channel noise, and the third part is the interference from the adjacent channel; is the total power factor value of all users.

3. The distributed full-duplex non-orthogonal multiple access vehicle network wireless energy transmission method according to claim 2, characterized in that: With the goal of minimizing the allocated power, considering the power allocation constraints, signal-to-noise ratio constraints, energy collection constraints, and power allocation factor constraints, the optimization model in step 2 is constructed as follows: Among them, C1 represents the power allocation threshold value, C2 represents the signal-to-noise ratio threshold value, C3 represents the energy collection threshold value, and C4 represents the value range constraint of the power allocation coefficient; among them, is the uplink transmission power allocated to user k, η k is the self-interference coefficient of user k; is the total power coefficient value of all users, ζ i represents the allocation power coefficient of user i, P k The power obtained from the transmitter for each user k, μ k represents the energy conversion efficiency of device k.

4. The distributed full-duplex non-orthogonal multiple access vehicle network wireless energy transmission method according to claim 3, characterized in that: In step 3, zero-forcing beamforming and dirty paper coding are used to eliminate multi-user interference, and the original optimization problem is further simplified. Problem P1 can be rewritten as: Among them, μ k represents the energy conversion efficiency of device k.

5. The distributed full-duplex non-orthogonal multiple access vehicle network wireless energy transmission method according to claim 4, characterized in that: The step A comprises the following steps: A1. If and only if When , a feasible solution to problem P2 exists; definition Convert the non-convex problem in P2 into a convex problem: A2. Obtain the optimal solution to the convex problem through the semi-definite relaxation technique based on the CVX solver 6. The distributed full-duplex non-orthogonal multiple access vehicle network wireless energy transmission method according to claim 5, characterized in that: Solving problem P3 according to the interior point method includes the following steps: a1. Initialization; a2. If Not feasible, exit; a3. If Using the interior point method to solve this problem, we get a4. Under the conditions of Perform eigenvalue decomposition to solve for the optimal beamforming vector a5. Obtain the optimal solution 7. The distributed full-duplex non-orthogonal multiple access vehicle network wireless energy transmission method according to claim 4, characterized in that: The step B comprises the following steps: B1. Assuming that the power allocation coefficient satisfies the equality constraint, problem P2 is transformed into: B2. Solve problem P4 using the analytical method and obtain the suboptimal solution of the power allocation coefficient and the beamforming vector.

8. The distributed full-duplex non-orthogonal multiple access vehicle network wireless energy transmission method according to claim 7, characterized in that: Solving problem P4 according to the analytical method includes the following sub-steps: b1. Initialization; b2. Let H\k=[h1,...,h1,h k+1 ,...,h K ], b3. If Not feasible, exit; b4. If make b5. Use Calculate the suboptimal solution separately

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